# A Genomic Framework for Molecular Risk Prediction & Individualized Lymphoma Therapy

> **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $561,946

## Abstract

PROJECT SUMMARY/ABSTRACT
PIs: Ash Alizadeh, M.D./Ph.D. & Maximilian Diehn, M.D./Ph.D.
For patients with Diffuse large B-cell lymphoma (DLBCL), the most common lymphoma subtype,
curative outcomes are common. Unfortunately, despite many large clinical trials, survival has not
significantly improved over the last 15 years and nearly a third of patients continue to succumb to
this disease. For these patients, effective strategies to predict early treatment failures have been
elusive.
Our long-term goal is to study the ability of baseline and dynamic risk factors, including genetic
mutations and circulating tumor DNA (ctDNA), to accurately predict treatment outcomes in DLBCL
patients. Our central hypothesis is that novel biomarkers of cancer risk, such as detection of
ctDNA and detailed genetic profiling, can be used for early detection of residual disease, to identify
dynamic changes that anticipate treatment failure, and to provide early surrogate endpoints for
future clinical trials. We will test our hypothesis via three specific aims: (1) To build an accurate
and dynamic predictor of survival for patients newly diagnosed with DLBCL, (2) To test the validity
and utility of this predictor in a large multi-institutional cohort of patients from around the globe,
and (3) To assess the ability of this dynamic risk assessment tool to serve as an early surrogate
endpoint in prospective clinical trials. We will apply our novel approach in both the frontline and
relapse/refractory setting and to a variety of treatment types including immunochemotherapy, an
antibody-drug conjugate and Chimeric Antigen Receptor (CAR) T cells.
If successful, our project will lead to novel ways to select better therapies for patients at highest
risk of failure. Our innovative approach, in which we will employ novel, blood-based methods for
tumor genotyping and disease monitoring that were developed by our group, will lay the
foundation for studies aimed at reducing risk of treatment failure in DLBCL patients.
Demonstrating that this approach can serve as a robust, early surrogate endpoint for patients with
aggressive lymphomas would be transformative for future trial design and for rapid evaluation of
novel, personalized treatment approaches in patients at highest risk for recurrence. Our work will
serve as proof-of-principle for an approach that could also be applied to other cancer types.

## Key facts

- **NIH application ID:** 9975772
- **Project number:** 5R01CA233975-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Ash Arash Alizadeh
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $561,946
- **Award type:** 5
- **Project period:** 2019-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9975772

## Citation

> US National Institutes of Health, RePORTER application 9975772, A Genomic Framework for Molecular Risk Prediction & Individualized Lymphoma Therapy (5R01CA233975-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9975772. Licensed CC0.

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